WO2015170105A1 - Méthodes de prédiction de l'hypernéphrome - Google Patents

Méthodes de prédiction de l'hypernéphrome Download PDF

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WO2015170105A1
WO2015170105A1 PCT/GB2015/051345 GB2015051345W WO2015170105A1 WO 2015170105 A1 WO2015170105 A1 WO 2015170105A1 GB 2015051345 W GB2015051345 W GB 2015051345W WO 2015170105 A1 WO2015170105 A1 WO 2015170105A1
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tumour
cell carcinoma
renal cell
individual
tissue
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PCT/GB2015/051345
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English (en)
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Ian Michael OVERTON
Grant Stewart
Alexander Lyulph Robert LUBBOCK
David James Harrison
Thomas Powles
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The University Court Of The University Of Edinburgh
Queen Mary University Of London
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57438—Specifically defined cancers of liver, pancreas or kidney
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/705—Assays involving receptors, cell surface antigens or cell surface determinants
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present invention relates to methods, uses and kits for predicting the prognosis and/or progression of Renal Cell Carcinoma in an individual, and methods, uses and kits for predicting the response to therapy of, and/or selecting a treatment for, Renal Cell Carcinoma in an individual.
  • Renal Cell Carcinoma is the most common type of kidney cancer in adults, in which it is responsible for approximately 90-95% of cases. Mortality is approximately 40%, and five-year survival for those with metastatic Renal Cell Carcinoma is ⁇ 10% (Stewart et a/., 201 1 , Nat Rev Urol., 8:255-265). There is a great unmet need for significant improvement in the treatment of localised and metastatic cancer as the disease remains the most lethal of all urological malignancies. Renal Cell Carcinoma typically originates in the lining of the proximal convoluted tubule.
  • Renal Cell Carcinoma is not a single entity, but is instead composed of different cell and tumour types derived from distinct parts of the nephron (such as the epithelium and/or renal tubules), each of which have distinct genotypes, gene expression profiles, histological features and clinical phenotypes.
  • Renal Cell Carcinoma such as sunitinib and axitinib
  • the lack of a molecular selection criteria means that the majority (around 70%) of Renal Cell Carcinoma patients are subjected to drugs without having any tumour response, whilst incurring potentially significant toxicity and cost (estimated at £70 million per annum in the ⁇ ).
  • the heterogeneous genotype and gene expression profile of Renal Cell Carcinoma tumours appears to increase following drug treatment (O'Mahony et al. (2013, J. Vis. Exp. (71 ), e50221 , doi:10.3791/50221 ), so it is even more difficult to identify relevant molecular markers in patients once treatment has started.
  • the present inventors have surprisingly discovered that the expression level of certain genes when taken in combination can be used as a indicator of the severity and progression of Renal Cell Carcinoma. That finding provides effective molecular markers for predicting the prognosis and/or progression of Renal Cell Carcinoma in an individual, and allows predictions to be made regarding the likely response to therapy of Renal Cell Carcinoma in an individual, thereby permitting an appropriate therapeutic treatment to be selected for that individual.
  • the present inventors used statistical machine-learning techniques to integrate clinical and pathological information with proteomic data to create a novel, robust prognostic and predictive algorithm in RCC.
  • the present invention therefore provides a general approach for predicting the prognosis and/or progression of Renal Cell Carcinoma in an individual, irrespective of whether treatment has commenced or not. Additionally, the invention provides an approach for predicting the response to therapy of Renal Cell Carcinoma in an individual, and provides a means for selecting an appropriate treatment for an individual with Renal Cell Carcinoma.
  • the invention provides a method for predicting the prognosis of Renal Cell Carcinoma in an individual, comprising the steps of: - providing a sample comprising one or more Renal Cell Carcinoma cell from the individual;
  • the invention provides a method for predicting the progression of Renal Cell Carcinoma in an individual, comprising the steps of:
  • the present invention provides a new molecular approach for assessing, characterising and monitoring Renal Cell Carcinoma in patients.
  • the inventor's findings are particularly surprising given the heterogeneity of gene expression in Renal Cell Carcinoma, and the invention will therefore be of real clinical benefit, for example in:
  • Identifying patients in whom a particular treatment (such as sunitinib) is likely to be effective, thereby guiding treatment decisions (particularly where patients are on the borderline of receiving that treatment due to other clinical factors), and providing additional confidence in the clinician's decision to treat.
  • a particular treatment such as sunitinib
  • Renal Cell Carcinoma is a well known disorder, and those skilled in the arts of medicine and oncology will be familiar with the associated symptoms and be capable of identifying and diagnosing the presence of Renal Cell Carcinoma in an individual.
  • medical practitioners expected an individual with Renal Cell Carcinoma to present with three findings - in particular: (1 ) haematuria; (2): flank pain; and (3): an abdominal mass - but it is now known that his triad of symptoms only occurs in 10-15% of cases, and is usually indicative of Renal Cell Carcinoma at an advanced stage.
  • Renal Cell Carcinoma typically include: blood in the urine (occurring in 40% of affected persons at the time that medical advice is sought); and/or flank pain (40%); and/or a mass in the abdomen or flank (25%); and/or weight loss (33%); and/or fever (20%); and/or high blood pressure (20%); and/or night sweats; and/or malaise. Renal Cell Carcinoma is also typically associated with a number of "paraneoplastic syndromes", which are conditions caused by either the hormones produced by the tumour itself or by the body's attack on the tumour, and which commonly affect tissues which do not actually house the tumour. The most common syndromes are selected from: anaemia or polycythaemia; and/or high blood calcium levels; and/or thrombocytosis; and/or secondary amyloidosis.
  • Renal Cell Carcinoma in an individual we include an individual that has been diagnosed as having Renal Cell Carcinoma, for example, due to the presentation of one or more of the associated symptoms as discussed herein.
  • Renal Cell Carcinoma is a general term that encompasses a range of distinct types of RCC, including: metastatic clear cell RCC; localised clear cell RCC; multilocular cystic clear cell RCC; tubulocystic RCC; thyroid-like follicular RCC; acquired cystic kidney disease-associated RCC; hybrid oncocytoma/chromophobe RCC.
  • the invention involves an individual that has, and is known to have, one or more type of RCC selected from the group comprising: metastatic clear cell RCC; localised clear cell RCC; multilocular cystic clear cell RCC; tubulocystic RCC; thyroid-like follicular RCC; acquired cystic kidney disease-associated RCC; hybrid oncocytoma/chromophobe RCC.
  • RCC metastatic clear cell
  • the individual has, and is known to have, metastatic clear cell RCC.
  • the individual Whilst it is preferred that the individual is a human, the individual may also be a non-human mammal (i.e. any mammal other than a human), such as, a horse, cow, goat, sheep, pig, dog, cat, rabbit, mouse or rat.
  • a particularly important aspect of the present invention is the inventors' finding that the expression level of at least three selected genes (of those listed in Table A) in combination provides an indicator of the severity and progression of Renal Cell Carcinoma.
  • Table A Genes used in the present invention.
  • the expression level of more than three genes selected from those listed in Table A could be used in the present invention - for example: four or more; or five or more; or six or more; or seven or more; or eight or more; or nine or more; or ten or more; or 11 or more; or 12 or more; or 13 or more; or 14 or more; or 15 or more; or 16 or more; or 17 or more; or 18 or more; or 19 or more; or 20 or more; or 21 or more; or 22 or more; or 23 or more; or 24 or more; or 25 or more; or 26 or more; or 27 or more; or 28 or more; or 29 or more; or 30 of the genes in Table A.
  • gene expression involves the steps of gene transcription (in which the gene coding sequence is transcribed to mRNA) and translation (in which mRNA is translated to form the encoded protein molecule).
  • Methods for measuring gene expression are known in the art and typically involve detecting the presence and/or activity of the product of transcription ⁇ i.e. mRNA), and/or the presence and/or activity of the product of translation ⁇ i.e. protein). Exemplary methods for detecting mRNA or protein associated with a particular gene are discussed below.
  • expression level we include a measure of the amount of mRNA and/or protein that is produced by gene expression. Expression may be quantified as the total amount of mRNA and/or protein detectable in a particular sample (such as in a single cell or group of cells), or the amount of mRNA and/or protein produced over a given period.
  • prognosis of Renal Cell Carcinoma in an individual we include the likely clinical development and outcome of Renal Cell Carcinoma in the individual, including the severity of the disease and/or the life expectancy or survival of the individual.
  • predicting the prognosis of Renal Cell Carcinoma in an individual we include the prediction of the likely clinical development and outcome of Renal Cell Carcinoma in the individual, including the likely severity of the disease and/or the likely life expectancy or survival of the individual.
  • the prognosis for Renal Cell Carcinoma was known to be influenced by a variety of factors, including tumour size, degree of invasion and metastasis, histologic type, and nuclear grade.
  • factors which may present a poor prognosis include a low "Karnofsky" performance-status score (a standard way of measuring functional impairment in patients with cancer), a low haemoglobin level, a high level of serum lactate dehydrogenase, and a high corrected level of serum calcium.
  • the "Leibovich" scoring system may be used to predict disease progression.
  • progression of Renal Cell Carcinoma in an individual we include the likely physical, cellular and/or molecular development of Renal Cell Carcinoma in the individual, including the progression between Stages and Grades of the disease.
  • predicting the prognosis of Renal Cell Carcinoma in an individual we include the prediction of the likely physical, cellular and/or molecular development of Renal Cell Carcinoma in the individual, including the likelihood of progression between Stages and Grades of the disease.
  • Staging and/or Grading may be used to classify and characterise Renal Cell Carcinoma in the methods of the invention.
  • Various staging approaches are known, such as the "TNM” staging system where the size and extent of the tumour (“T”), involvement of lymph nodes (“N”) and metastases (“M”) are classified separately.
  • T size and extent of the tumour
  • N lymph nodes
  • M metastases
  • an overall stage grouping into Stage l-IV can be used, in line with various clinical guidelines, as shown below:
  • Grading may be performed using the "Fuhrman system", which is an assessment based on the microscopic morphology of a neoplasm, using haematoxylin and eosin (H&E staining). That system categorises Renal Cell Carcinoma with Grades 1-4 based on nuclear characteristics, as shown below:
  • the step of predicting prognosis or progression of Renal Cell Carcinoma in the individual is additionally based on the age of the individual.
  • the inventors have found that the age of the individual is associated with prognosis and/or progression of Renal Cell Carcinoma in that individual.
  • increasing age correlates with a poor prognosis whilst decreasing age correlates with a good prognosis; and increasing age correlates with an increased likelihood of progression whilst decreasing age correlates with a decreased likelihood of progression.
  • older individuals have a greater risk of a poor prognosis than younger individuals.
  • the age of the individual is 38 years old, or older; for example: 50 years old or older; or 60 years old or older; or 70 years old or older; or 80 years old or older; or 90 years old or older.
  • the individual is aged between 38 years old and 79 years old. As discussed above and demonstrated in the accompanying Examples, an older individual has a greater risk of a poor prognosis than a younger individual.
  • the individual has been treated, or is being treated, with one or more anti-Renal Cell Carcinoma treatment. In an alternative embodiment, the individual has not previously been treated with an anti-Renal Cell Carcinoma treatment.
  • Renal Cell Carcinoma treatments are known and used to manage the disorder and alleviate the associated symptoms.
  • Renal Cell Carcinoma may be treated with radiation therapy and/or chemotherapy, although many cases are relatively resistant to such therapies and immunotherapy is often preferable. As will be appreciate, all such treatments carry the risk of drug toxicity which can lead to undesirable and unpleasant side-effects in the treated individual.
  • the anti-Renal Cell Carcinoma treatment comprises or consists of a Tyrosine Kinase Inhibitor (such as a Receptor Tyrosine Kinase inhibitor) or an mTOR Inhibitor.
  • a Tyrosine Kinase Inhibitor such as a Receptor Tyrosine Kinase inhibitor
  • an mTOR Inhibitor is well known to those in the art of medicine.
  • the Tyrosine Kinase Inhibitor is selected from the group comprising: sunitinib; sorafenib; bevacizumab; pazopanib; axitinib.
  • the mTOR Inhibitor is selected from the group comprising: temsirolimus; everolimus.
  • the anti-Renal Cell Carcinoma treatment is sunitinib.
  • sunitinib is administered in a six-week treatment cycle comprising four-weeks of continuous treatment of 50mg sunitinib administered once per day, followed by two-weeks of no treatment.
  • the dosage may be adjusted in steps of 12.5mg according to tolerability.
  • Sunitinib and pazopanib are typically used as first-line treatment in many individuals with Renal Cell Carcinoma.
  • Axitinib is typically used as a second-line treatment in individuals with Renal Cell Carcinoma, for example, when the individual is not responsive to sunitinib and/or pazopanib or has developed resistance to sunitinib and/or pazopanib.
  • the present invention may be performed on an individual that has not been treated with an anti-Renal Cell Carcinoma treatment, or may be performed on an individual that has been treated and/or is being treated, with an anti-Renal Cell Carcinoma treatment.
  • the present invention may be used to predict the prognosis and/or progression of Renal Cell Carcinoma in an individual that has been treated, and/or is being treated, with an anti-Renal Cell Carcinoma treatment (such as sunitinib or pazopanib).
  • an anti-Renal Cell Carcinoma treatment such as sunitinib or pazopanib.
  • the invention therefore provides a method for determining whether the treatment is therapeutically effective for that individual, and therefore allows a decision to be made regarding the future treatment of that individual.
  • sunitinib For example, where the individual has been treated, or is being treated, with sunitinib and it is determined not to be therapeutically effective, another treatment (such as pazopanib or axitinib) can be administered to the individual instead of, or in addition to, sunitinib.
  • another treatment such as pazopanib or axitinib
  • the invention provides a method wherein the Renal Cell Carcinoma is characterised in that it comprises one or more of the following: a Stage I tumour; a Stage II tumour; a Stage III tumour, a Stage IV tumour.
  • the invention provides a method wherein the Renal Cell Carcinoma is characterised in that it comprises one or more of the following: a Grade 1 tumour; a Grade 2 tumour; a Grade 3 tumour; a Grade 4 tumour.
  • the VHL mutation a known mutation that predisposes to multiple cancers, including Renal Cell Carcinoma
  • the present invention is applicable to Renal Cell Carcinoma regardless of the VHL gene status of the cancer, and may therefore be performed on Renal Cell Carcinoma which has the VHL mutation or does not have the VHL mutation.
  • the step of predicting the prognosis or progression of Renal Cell Carcinoma in the individual is based on the expression level of the at least three genes, relative to a control.
  • control comprises a standard level of expression level of the same at least three genes that are analysed in the sample from the individual being tested. For example, where the expression level of N-Cadherin and EpCAM and mTOR is determined in the sample, the control comprises a standard level of expression of the N-Cadherin and EpCAM and mTOR genes.
  • the control comprises one or more Renal Cell Carcinoma cell known to be associated with a poor prognosis or progression, and/or is resistant to an anti-Renal Cell Carcinoma treatment.
  • the control comprises Renal Cell Carcinoma cells obtained from a group of individuals.
  • the control comprises one or more Renal Cell Carcinoma cell known to be associated with a favourable prognosis or progression, and/or is treatable with an anti-Renal Cell Carcinoma treatment.
  • the control may comprise one or more corresponding renal cell which is not cancerous.
  • it is preferred that the control comprises renal cells obtained from a group of individuals.
  • a “group of individuals” we mean a group comprising two or more individuals, and preferably four or more individuals. Ideally, the group will not exceed 10,000 individuals, and will preferably not exceed 1000 individuals. Preferably, the group comprises or consists of between 4 and 100 individuals. Preferably, the control will be one that is appropriately matched with the individual being tested - for example, in terms of being the same sex and/or of similar age and/or smoking status. It is preferred in the methods of the invention that the at least three genes comprise or consist of: N-cadherin, EpCAM and mTOR.
  • the at least three genes comprise or consist of: N-cadherin, EpCAM and mTOR, and that the method of the invention is additionally based on the age of the individual.
  • the value assigned to the expression level of N-cadherin, EpCAM and mTOR is an estimate of the concentration of that protein relative to total protein or to the concentration of a specific control protein (such as pan-cytokeratin), as described in the accompanying Examples.
  • BIC Bayesian Information Criterion
  • Backward elimination iteratively removes a single feature at each step, selected for the greatest improvement in BIC value. Thus, features with low predictive power or high redundancy are removed. The procedure terminates with a final model when removing any single feature does not improve the BIC value.
  • the function 'stepAIC is used from a MASS R library, with the value of k (a multiplier penalising model complexity) specified for BIC regularisation (Venables and Ripley (2002)
  • a total of 12 features are input to wrapper selection; these may include key clinical parameters where data is available for the cohorts (such as: grade, gender, age, neutrophils, haemoglobin level, DCM score (Lancet Oncol 2013; 14: 141)).
  • Other features that may be included are the median tumour expression of proteins that are significantly differentially expressed and/or have substantively increased variance upon treatment (for example: BCL2, MLH1 , CAIX, mTOR, N-cadherin and EpCAM).
  • the baseline hazard ho(t) represents the intercept and the hazard where all covariate values are 0.
  • Cox modelling assumes that covariates are related multiplicatively to hazard, which is known as the proportionality assumption. It is important that the proportionality assumption is met, and the scaled Schoenfield residuals test is appropriate for this (Biometrika 1994;81 :515, Stat Med 1997; 16:61 1 ). For further discussion see BJC 2003;89:431 and BJC 2003;89:605.
  • a Hazard of >1 indicates a poor prognosis or progression
  • a Hazard of ⁇ 1 indicates a favourable prognosis or progression.
  • a Hazard of >1 indicates a poor prognosis and a high risk/likelihood that the Renal Cell Carcinoma will progress to a more serious or severe stage and/or grade
  • a Hazard of ⁇ 1 indicates a good prognosis and a low risk/likelihood that the Renal Cell Carcinoma will progress to a more serious or severe stage and/or grade.
  • Those skilled in the art will be familiar with the concept of assessing risk using a "Hazard" value.
  • Hazard describes the probability of an individual experiencing an event - as used herein, in the context of survival
  • Hazard describes the instantaneous death rate for an individual who is alive at a given time.
  • the method of Cox proportional hazards regression is a popular approach for multivariate modelling of survival data, and that method takes as input a set of variables under consideration (covariates) and patient survival times.
  • the step of predicting the prognosis or progression of Renal Cell Carcinoma in the individual comprises predicting one or more of the following: percentage response of the individual to anti-Renal Cell Carcinoma treatment; overall survival of the individual; disease-specific survival of the individual; and/or progression-free survival of the individual.
  • “Overall survival of the individual” refers to the length of time that the individual survives, regardless of the cause of death.
  • Disease-specific survival of the individual refers to the length of time that the individual survives, where the cause of death is the disease being monitored (and in which individuals that die from other causes are disregarded).
  • the "disease-specific survival rate” is the percentage of individuals in a study who have not died from the disease in a defined period of time; the time period usually begins at the time of diagnosis or at the start of treatment and ends at the time of death.
  • Progression-free survival of the individual refers to the length of time during and after the diagnosis and any treatment, that the individual lives with the disease without it worsening.
  • Methods of measuring the expression level of a gene are well known to those in the art of molecular biology.
  • the expression level is determined by measuring the presence and/or amount of one or more product of the gene, for example: protein or mRNA.
  • Assaying protein levels in a biological sample can be performed using any art-known method.
  • Preferred for assaying protein levels in a biological sample are antibody-based techniques. Such techniques may involve a primary antibody (which specifically recognises the target protein) and a secondary antibody (which specifically recognises the primary antibody) which comprises a detectable moiety.
  • antibody-based methods useful for detecting protein levels include immunoassays, such as the enzyme linked immunosorbent assay (ELISA) and the radioimmunoassay (RIA).
  • ELISA enzyme linked immunosorbent assay
  • RIA radioimmunoassay
  • a protein-specific monoclonal antibody can be used both as an immune-adsorbent and as an enzyme-labelled probe to detect and quantify the protein.
  • the amount of protein present in the sample can be calculated by reference to the amount present in a standard preparation using a linear regression computer algorithm.
  • ELISA enzyme linked immunosorbent assay
  • RIA radioimmunoassay
  • a protein-specific monoclonal antibody can be used both as an immune-adsorbent and as an enzyme-labelled probe to detect and quantify the protein.
  • the amount of protein present in the sample can be calculated by reference to the amount present in a standard preparation using a linear regression computer algorithm.
  • Such an ELISA for detecting a tumour antigen is described in lacobelli
  • the above techniques may be conducted essentially as a "one-step” or “two-step” assay.
  • the "one-step” assay involves contacting protein with immobilized antibody and, without washing, contacting the mixture with the labelled antibody.
  • the "two-step” assay involves washing before contacting the mixture with the labelled antibody.
  • Other conventional methods may also be employed as suitable. It is usually desirable to immobilize one component of the assay system on a support, thereby allowing other components of the system to be brought into contact with the component and readily removed from the sample.
  • Suitable enzyme labels include, for example, those from the oxidase group, which catalyse the production of hydrogen peroxide by reacting with substrate.
  • Glucose oxidase is particularly preferred as it has good stability and its substrate (glucose) is readily available.
  • Activity of an oxidase label may be assayed by measuring the concentration of hydrogen peroxide formed by the enzyme-labelled antibody/substrate reaction.
  • suitable labels include radioisotopes, such as iodine (1251, 1211), carbon (14C), sulphur 35S), tritium (3H), indium (112ln), and technetium (99mTc), and fluorescent labels, such as fluorescein and rhodamine, and biotin.
  • protein-specific antibodies for use in the present invention can be raised against the intact protein or an antigenic polypeptide fragment thereof, which may be presented together with a carrier protein, such as an albumin, to an animal system (such as rabbit or mouse) or, if it is long enough (at least about 25 amino acids), without a carrier.
  • a carrier protein such as an albumin
  • the term “antibody” (Ab) or “monoclonal antibody” (Mab) is meant to include intact molecules as well as antibody fragments (such as, for example, Fab and F(ab')2 fragments) which are capable of specifically binding to the target protein.
  • Fab and F(ab')2 fragments lack the Fc fragment of intact antibody, clear more rapidly from the circulation, and may have less non-specific tissue binding of an intact antibody (Wahl et al_, J. Nucl. Med. 24:316-325 (1983)). Thus, these fragments are preferred.
  • suitable labels for protein-specific antibodies are provided below.
  • suitable enzyme labels include malate dehydrogenase, staphylococcal nuclease, delta-5- steroid isomerase, yeast-alcohol dehydrogenase, alpha-glycerol phosphate dehydrogenase, triose phosphate isomerase, peroxidase, alkaline phosphatase, asparaginase, glucose oxidase, beta-galactosidase, ribonuclease, urease, catalase, glucose-6-phosphate dehydrogenase, glucoamylase, and acetylcholine esterase.
  • radio-isotopic labels examples include 3H, 1111n, 1251, 1311, 32P, 35S, 14C, 51 Cr, 57To , 58Co, 59Fe , 75Se, 152Eu, 90Y, 67Cu, 217Ci, 211 At, 212Pb , 47Sc, and 109Pd.
  • suitable non-radioactive isotopic labels include 157Gd, 55Mn, 162Dy, 52Tr, and 56Fe.
  • fluorescent labels examples include an 152Eu label, a fluorescein label, an isothiocyanate label, a rhodamine label, a phycoerythrin label, a phycocyanin label, an allophycocyanin label, an o-phthaldehyde label, and a fluorescamine label.
  • suitable toxin labels include diphtheria toxin, ricin, and cholera toxin.
  • chemiluminescent labels include a luminal label, an isoluminal label, an aromatic acridinium ester label, an imidazole label, an acridinium salt label, an oxalate ester label, a luciferin label, a luciferase label, and an aequorin label.
  • nuclear magnetic resonance contrasting agents include heavy metal nuclei such as Gd, Mn, and iron.
  • Typical techniques for binding the above-described labels to antibodies are provided by Kennedy et al., Clin. Chim. Acta 70:1-31 (1976), and Schurs et al, Clin. Chim. Acta 81:1- 40 (1977). Coupling techniques mentioned in the latter are the glutaraldehyde method, the periodate method, the dimaleintide method, the m-maleimidobenzyl-N-hydroxy- succinimide ester method, all of which methods are incorporated by reference herein.
  • protein is measured using a method selected from the list comprising: Raman spectroscopy; Acoustic Membrane MicroParticle technology; an antibody-based detection method, for example, RPPA or AQUA.
  • AMP Acoustic Membrane MicroParticle
  • mRNA is measured using a PCR-based approach, for example RT-PCR.
  • RT-PCR a PCR-based approach
  • the RT-PCR method is described in Makino et al, Technique 2:295-301 (1990), and involves the radio-activities of the "amplicons" in the polyacrylamide gel bands being linearly related to the initial concentration of the target mRNA. Briefly, that method involves adding total RNA isolated from a biological sample in a reaction mixture containing a RT primer and appropriate buffer. After incubating for primer annealing, the mixture can be supplemented with a RT buffer, dNTPs, DTT, RNase inhibitor and reverse transcriptase.
  • RNA After incubation to achieve reverse transcription of the RNA, the RT products are subjected to PCR using labelled primers. Alternatively, rather than labelling the primers, a labelled dNTP can be included in the PCR reaction mixture.
  • PCR amplification can be performed in a DNA thermal cycler according to conventional techniques. After a suitable number of rounds to achieve amplification, the PCR reaction mixture is electrophoresed on a polyacrylamide gel. After drying the gel, the radioactivity of the appropriate bands (corresponding to the mRNA) is quantified using an imaging analyser.
  • RT and PCR reaction ingredients and conditions, reagent and gel concentrations, and labelling methods are well known in the art. Variations on the RT-PCR method will be apparent to those skilled in the art.
  • the method of the first and/or second aspect of the invention further comprises the step of selecting a treatment for the individual.
  • the method of the first and/or second aspect of the invention further comprises the step of administering the selected treatment to the individual.
  • the present invention may be used to predict the prognosis and/or progression of Renal Cell Carcinoma in an individual that has been treated, or is being treated, with an anti-Renal Cell Carcinoma treatment (such as sunitinib or pazopanib).
  • an anti-Renal Cell Carcinoma treatment such as sunitinib or pazopanib.
  • the invention therefore provides a method for determining whether the treatment is therapeutically effective for that individual, and therefore allows a decision to be made regarding the future treatment of that individual.
  • the anti-Renal Cell Carcinoma treatment comprises or consists of a Tyrosine Kinase Inhibitor or an mTOR Inhibitor. Such inhibitors are well known to those in the art of medicine.
  • the Tyrosine Kinase Inhibitor is selected from the group comprising: sunitinib; sorafenib; bevacizumab; pazopanib; axitinib.
  • the mTOR Inhibitor is selected from the group comprising: temsirolimus; everolimus.
  • the anti-Renal Cell Carcinoma treatment is sunitinib.
  • sunitinib is administered in a six-week treatment cycle comprising four-weeks of continuous treatment of 50mg sunitinib administered once per day, followed by two-weeks of no treatment.
  • the dosage may be adjusted in steps of 12.5mg according to tolerability.
  • the sample is selected from the group comprising: a tumour biopsy; blood; serum; plasma; lymphatic fluid; urine.
  • Clinical approaches for obtaining such samples from an individual will be known to those skilled in the art of medicine.
  • the sample is a tumour biopsy.
  • Samples of bodily fluids such as blood; serum; plasma; lymphatic fluid; urine
  • CTCs Circulating Tumour Cells derived from the Renal Cell Carcinoma, which may be purified using standard techniques known to those in the art (for example, Nagrath er a/., 2007, Nature, 450:1235-1239).
  • the inventors have identified that, where the methods of the invention are performed using a tumour biopsy sample, the results are of greater significance if the sample contains tissue from two or more (and preferably, three or more) distinct parts of the tumour.
  • Renal Cell Carcinoma tumours exhibit significant heterogeneity across the tumour, and the inventors' approach of generating a "combined" or "averaged” sample, is thought to provide a sample that is a better representation of the tumour as a whole - by doing so, the methods of the invention are less likely to be based on an atypical part of the tumour, and the results consequently have greater significance and accuracy.
  • the sample is a tumour biopsy and the sample comprises tissue from two or more distinct parts of the tumour. It will be appreciated that the tissue will comprise one or more Renal Cell Carcinoma cell from the part of the tumour from which it is taken.
  • the sample comprises tissue from three or more distinct parts of the tumour.
  • the sample may comprise tissue from four or more distinct parts of the tumour; or tissue from five or more distinct parts of the tumour; or tissue from six or more distinct parts of the tumour; or tissue from seven or more distinct parts of the tumour; or tissue from eight or more distinct parts of the tumour; or tissue from nine or more distinct parts of the tumour; or tissue from ten or more distinct parts of the tumour.
  • each sample comprises 1 cubic centimetre (i.e. 1cm3) volume of tissue, or more (such as 2cm3 of tissue; or 3cm3 of tissue).
  • tissue from the relevant parts of the tumour is obtained by taking a separate biopsy from each distinct part of the tumour.
  • tissue from the relevant parts of the tumour may be obtained by taking a single biopsy which comprises distinct part of the tumour.
  • the step of determining the expression level of the selected genes is performed separately in each biopsy.
  • the parts of the tumour are distinguished from each other, for example, on the basis of one or more of the following: tumour morphology (such as Fuhrman grade and/or sarcomatoid features and/or necrosis); and/or physical location within the tumour.
  • tumour morphology such as Fuhrman grade and/or sarcomatoid features and/or necrosis
  • physical location within the tumour e.g.
  • tumours may have differences in terms of oxygenation, tumour cell biology (for example, invasive character and/or differentiation status), and composition of the stromal components;
  • Suitable approaches are known in the art for selecting and taking tissue from distinct parts of a tumour in order to generate a tumour biopsy sample for use in the invention.
  • the parts of the tumour may be randomly selected from within the tumour.
  • the parts of the tumour may be purposely selected from distinct regions of the tumour - for example, to ensure that certain distinct parts of the tumour are selected for analysis, as discussed above.
  • a particularly-preferred embodiment of the first aspect of the invention is: a method for predicting the prognosis of Renal Cell Carcinoma (RCC) in an individual, comprising the steps of:
  • the at least three genes comprise: N-cadherin, EpCAM and mTOR; and wherein the sample is a tumour biopsy and the sample comprises tissue from two or more (and, preferably, three or more) parts of the tumour.
  • a particularly-preferred embodiment of the second aspect of the invention is: a method for predicting the progression of Renal Cell Carcinoma in an individual, comprising the steps of:
  • the at least three genes comprise: N-cadherin, EpCAM and mTOR; and wherein the sample is a tumour biopsy and the sample comprises tissue from two or more (and, preferably, three or more) parts of the tumour.
  • the invention provides a method for predicting the response to therapy of Renal Cell Carcinoma in an individual, comprising the steps of: - providing a sample comprising one or more Renal Cell Carcinoma cell from the individual;
  • the invention provides a method for selecting a treatment for an individual with Renal Cell Carcinoma, comprising the steps of: - providing a sample comprising one or more Renal Cell Carcinoma cell from the individual;
  • the step of "providing a sample comprising one or more Renal Cell Carcinoma cell from the individual” and "determining in the one or more cell the expression level of at least three genes selected from those listed in Table A" may be performed as described above in relation to the first or second aspects of the invention. It will be appreciated that any combination of at least three genes from those listed in Table A could be selected and used in the third and/or fourth aspects of the invention.
  • the expression level of more than three genes selected from those listed in Table A could be used in the present invention - for example: four or more; or five or more; or six or more; or seven or more; or eight or more; or nine or more; or ten or more; or 11 or more; or 12 or more; or 13 or more; or 14 or more; or 15 or more; or 16 or more; or 17 or more; or 18 or more; or 19 or more; or 20 or more; or 21 or more; or 22 or more; or 23 or more; or 24 or more; or 25 or more; or 26 or more; or 27 or more; or 28 or more; or 29 or more; or 30 of the genes in Table A.
  • the inventors' findings provide an approach for predicting the prognosis and/or progression of Renal Cell Carcinoma in an individual that has been treated, or is being treated, with an anti-Renal Cell Carcinoma treatment (such as sunitinib or pazopanib).
  • an anti-Renal Cell Carcinoma treatment such as sunitinib or pazopanib.
  • the invention provides a method for determining whether the treatment is therapeutically effective for that individual, and therefore allows a decision to be made regarding the future treatment of that individual.
  • the RECIST Response Evaluation Criteria In Solid Tumours
  • RECIST Response Evaluation Criteria In Solid Tumours
  • sunitinib For example, where the individual has been treated, or is being treated, with sunitinib and it is determined not to be therapeutically effective, another treatment (such as pazopanib or axitinib) can be administered to the individual instead, or in addition to, sunitinib.
  • another treatment such as pazopanib or axitinib
  • the step of predicting the response to therapy or selecting a treatment for the individual is additionally based on the age of the individual.
  • the age of the individual is associated with prognosis and/or progression of Renal Cell Carcinoma in that individual.
  • increasing age correlates with a poor prognosis whilst decreasing age correlates with a good prognosis; and increasing age correlates with an increased likelihood of progression whilst decreasing age correlates with a decreased likelihood of progression.
  • older individuals have a greater risk of a poor prognosis than younger individuals.
  • the individual has been treated, or is being treated, with one or more anti-Renal Cell Carcinoma treatment.
  • the individual has not previously been treated with an anti-Renal Cell Carcinoma treatment.
  • the invention provides a method wherein the Renal Cell Carcinoma is characterised in that it comprises one or more of the following: a Stage I tumour; a Stage II tumour; a Stage III tumour; a Stage IV tumour.
  • the invention provides a method wherein the Renal Cell Carcinoma is characterised in that it comprises one or more of the following: a Grade 1 tumour; a Grade 2 tumour; a Grade 3 tumour; a Grade 4 tumour.
  • the step of predicting the response to therapy or selecting a treatment for the individual is based on the expression level of the at least three genes, relative to a control.
  • control comprises a standard level of expression level of the same at least three genes that are analysed in the sample from the individual being tested. For example, where the expression level of N-Cadherin and EpCAM and mTOR is determined in the sample, the control comprises a standard level of expression of the N-Cadherin and EpCAM and mTOR genes.
  • control comprises one or more Renal Cell Carcinoma cell known to be associated with a poor prognosis or progression, and/or is resistant to an anti-Renal Cell Carcinoma treatment.
  • the control comprises Renal Cell Carcinoma cells obtained from a group of individuals.
  • the control comprises one or more Renal Cell Carcinoma cell known to be associated with a favourable prognosis or progression, and/or is treatable with an anti-Renal Cell Carcinoma treatment.
  • the control may comprise one or more corresponding renal cell which is not cancerous.
  • the control comprises renal cells obtained from a group of individuals.
  • the at least three genes comprise: N-cadherin, EpCAM and mTOR.
  • the at least three genes comprise or consist of: N-cadherin, EpCAM and mTOR, and that the method of the invention is additionally based on the age of the individual.
  • the step of predicting the response to therapy of Renal Cell Carcinoma in the individual, or the step of selecting a treatment for the individual is based on the algorithm:
  • a Hazard of >1 indicates a poor prognosis or progression
  • a Hazard of ⁇ 1 indicates a favourable prognosis or progression
  • the value assigned to the expression level of N-cadherin, EpCAM and mTOR is an estimate of the concentration of that protein relative to total protein or to the concentration of a specific control protein (such as pan-cytokeratin), as described in the accompanying Examples.
  • the expression level is determined by measuring the presence and/or amount of one or more product of the gene, for example: protein or mRNA.
  • protein is measured using a method selected from the list comprising: Raman spectroscopy; Acoustic Membrane MicroParticle technology; an antibody-based detection method, for example, RPPA orAQUA.
  • mRNA is measured using a PCR-based approach, for example RT-PCR.
  • the method of the third aspect of the invention further comprises the step of selecting a treatment for the individual.
  • the method of the third or fourth aspect of the invention further comprise the step of administering the selected treatment to the individual.
  • the treatment selected and/or administered in the methods of third or fourth aspects of the invention is an anti-Renal Cell Carcinoma treatment, such as a Tyrosine Kinase Inhibitor or an mTOR Inhibitor.
  • Anti-Renal Cell Carcinoma treatments, and such inhibitors are known to those skilled in the art.
  • the Tyrosine Kinase Inhibitor is selected from the group comprising: sunitinib; sorafenib; bevacizumab; pazopanib; axitinib.
  • the mTOR Inhibitor is selected from the group comprising: temsirolimus; everolimus.
  • the anti-Renal Cell Carcinoma treatment is sunitinib.
  • sunitinib is administered in a six-week treatment cycle comprising four-weeks of continuous treatment of 50mg sunitinib administered once per day, followed by two-weeks of no treatment.
  • the dosage may be adjusted in steps of 12.5mg according to tolerability.
  • the sample is selected from the group comprising: a tumour biopsy; blood; serum; plasma; lymphatic fluid; urine. Details of those sample types are discussed above.
  • the inventors have identified that, where the methods of the invention are performed using a tumour biopsy sample, the results are of greater significance if the sample contains tissue from two or more (and preferably, three or more) distinct parts of the tumour.
  • Renal Cell Carcinoma tumours exhibit significant heterogeneity across the tumour, and the inventors' approach of generating a "combined" or “averaged” sample, is thought to provide a sample that is a better representation of the tumour as a whole - by doing so, the methods of the invention are less likely to be based on an atypical part of the tumour, and the results consequently have greater significance and accuracy.
  • the sample is a tumour biopsy and the sample comprises tissue from two or more distinct parts of the tumour. It will be appreciated that the tissue will comprise one or more Renal Cell Carcinoma cell from the part of the tumour from which it is taken.
  • the sample comprises tissue from three or more distinct parts of the tumour.
  • the sample may comprise tissue from four or more distinct parts of the tumour; or tissue from five or more distinct parts of the tumour; or tissue from six or more distinct parts of the tumour; or tissue from seven or more distinct parts of the tumour; or tissue from eight or more distinct parts of the tumour; or tissue from nine or more distinct parts of the tumour; or tissue from ten or more distinct parts of the tumour.
  • each sample comprises 1 cubic centimetre (i.e. 1 cm3) volume of tissue, or more (such as 2cm3 of tissue; or 3cm3 of tissue).
  • tissue from the relevant parts of the tumour is obtained by taking a separate biopsy from each distinct part of the tumour.
  • tissue from the relevant parts of the tumour may be obtained by taking a single biopsy which comprises distinct part of the tumour.
  • the step of determining the expression level of the selected genes is performed separately in each biopsy.
  • the parts of the tumour are distinguished from each other, for example, on the basis of one or more of the following: tumour morphology (such as Fuhrman grade and/or sarcomatoid features and/or necrosis); and/or physical location within the tumour.
  • tumour morphology such as Fuhrman grade and/or sarcomatoid features and/or necrosis
  • Suitable approaches are known in the art for selecting and taking tissue from distinct parts of a tumour in order to generate a tumour biopsy sample for use in the invention.
  • the parts of the tumour may be randomly selected from within the tumour.
  • the parts of the tumour may be purposely selected from distinct regions of the tumour - for example, to ensure that certain distinct parts of the tumour are selected for analysis, as discussed above.
  • a particularly-preferred embodiment of the third aspect of the invention is: a method for predicting the response to therapy of Renal Cell Carcinoma in an individual, comprising the steps of:
  • the at least three genes comprise: N-cadherin, EpCAM and mTOR; and wherein the sample is a tumour biopsy and the sample comprises tissue from two or more (and, preferably, three or more) parts of the tumour.
  • a particularly-preferred embodiment of the fourth aspect of the invention is: a method for selecting a treatment for an individual with Renal Cell Carcinoma, comprising the steps of:
  • the at least three genes comprise: N-cadherin, EpCAM and mTOR; and wherein the sample is a tumour biopsy and the sample comprises tissue from two or more (and, preferably, three or more) parts of the tumour.
  • the invention provides methods and uses for characterising and classifying Renal Cell Carcinoma cells in an individual, and provides a means for identifying cells that are associated with poor prognosis and/or resistance to sunitinib. Such cells may be useful as tools in methods for identifying alternative agents that can be used as effective therapeutics against such cells.
  • the invention provides a method for identifying an agent suitable for treating Renal Cell Carcinoma, comprising the steps of: providing an agent to be tested;
  • the sample is a tumour biopsy and the sample comprises tissue from two or more distinct parts of the tumour. It will be appreciated that the tissue will comprise one or more Renal Cell Carcinoma cell from the part of the tumour from which it is taken.
  • the sample comprises tissue from three or more distinct parts of the tumour.
  • the sample may comprise tissue from four or more distinct parts of the tumour; or tissue from five or more distinct parts of the tumour; or tissue from six or more distinct parts of the tumour; or tissue from seven or more distinct parts of the tumour; or tissue from eight or more distinct parts of the tumour; or tissue from nine or more distinct parts of the tumour; or tissue from ten or more distinct parts of the tumour.
  • each sample comprises 1 cubic centimetre (i.e. 1 cm3) volume of tissue, or more (such as 2cm3 of tissue; or 3cm3 of tissue).
  • tissue from the relevant parts of the tumour is obtained by taking a separate biopsy from each distinct part of the tumour.
  • tissue from the relevant parts of the tumour may be obtained by taking a single biopsy which comprises distinct part of the tumour.
  • the step of determining the expression level of the selected genes is performed separately in each biopsy.
  • the parts of the tumour are distinguished from each other, for example, on the basis of one or more of the following: tumour morphology (such as Fuhrman grade and/or sarcomatoid features and/or necrosis); and/or physical location within the tumour.
  • tumour morphology such as Fuhrman grade and/or sarcomatoid features and/or necrosis
  • tumours may have differences in terms of oxygenation, tumour cell biology (for example, invasive character and/or differentiation status), and composition of the stromal components;
  • Suitable approaches are known in the art for selecting and taking tissue from distinct parts of a tumour in order to generate a tumour biopsy sample for use in the invention.
  • the parts of the tumour may be randomly selected from within the tumour.
  • the parts of the tumour may be purposely selected from distinct regions of the tumour - for example, to ensure that certain distinct parts of the tumour are selected for analysis, as discussed above.
  • Assays suitable for determining proliferation and/or differentiation of the one or more cell are well known to those in the art of cell biology.
  • one or more of the following assays may be used: a 2D invasion assay (for example, scratch plate and/or transwell); a 3D invasion assay (for example, as described in PLoS One 2011 ;6:e17083); an apoptosis assay; a cell viability assay; a growth inhibition assay; a vascularisation assay (for example, as described in Microvasc. Res 2014;92:72)
  • Kits suitable for performing such assays are commercially available and may be sourced from, for example, Roche, Cell Biolabs, and/or Millipore.
  • the expression level of more than three genes selected from those listed in Table A could be used in the present invention - for example: four or more; or five or more; or six or more; or seven or more; or eight or more; or nine or more; or ten or more; or 11 or more; or 12 or more; or 13 or more; or 14 or more; or 15 or more; or 16 or more; or 17 or more; or 18 or more; or 19 or more; or 20 or more; or 21 or more; or 22 or more; or 23 or more; or 24 or more; or 25 or more; or 26 or more; or 27 or more; or 28 or more; or 29 or more; or 30 of the genes in Table A.
  • the at least three genes comprise: N-cadherin, EpCAM and mTOR.
  • the method of the fifth aspect of the invention further comprises the step of manufacturing the identified agent. In a further preferred embodiment, the method of the fifth aspect of the invention further comprises the step of formulating the identified agent into a pharmaceutical composition.
  • the Renal Cell Carcinoma is metastatic clear cell Renal Cell Carcinoma. Typically, in metastatic Renal Cell Carcinoma, the cancer has metastasised to one or more of the following: lymph node; lung; liver; adrenal gland; brain; bone.
  • the invention provides a kit for performing the method of the first and/or second and/or third and/or fourth aspect of the invention, the kit comprising: one or more reagent for determining the expression level of at least three genes selected from those listed in Table A; and
  • control sample comprising a standard level of expression level of the same at least three genes as defined in (i), above.
  • the one or more control sample comprises one or more Renal Cell Carcinoma cell known to be associated with a poor prognosis or progression, and/or is resistant to an anti-Renal Cell Carcinoma treatment.
  • the control comprises Renal Cell Carcinoma cells obtained from a group of individuals.
  • the control comprises one or more Renal Cell Carcinoma cell known to be associated with a favourable prognosis or progression, and/or is treatable with an anti-Renal Cell Carcinoma treatment.
  • that control may comprise one or more corresponding renal cell which is not cancerous.
  • the control comprises renal cells obtained from a group of individuals.
  • the one or more reagent for determining the expression level is an antibody, and is preferably one or more antibody selected from those listed in Table 2. It will be appreciated that the kit may comprise one or more reagent for determining the expression level of any combination of at least three genes from those listed in Table A.
  • the kit may comprise one or more reagent for determining the expression level of more than three genes selected from those listed in Table A - for example: four or more; or five or more; or six or more; or seven or more; or eight or more; or nine or more; or ten or more; or 11 or more; or 12 or more; or 13 or more; or 14 or more; or 15 or more; or 16 or more; or 17 or more; or 18 or more; or 19 or more; or 20 or more; or 21 or more; or 22 or more; or 23 or more; or 24 or more; or 25 or more; or 26 or more; or 27 or more; or 28 or more; or 29 or more; or 30 of the genes in Table A.
  • the at least three genes comprises or consists of N-Cadherin and EpCAM and mTOR. More preferably, the one or more reagent for determining the expression level comprises: an antibody against N-Cadherin, and an antibody against EpCAM, and an antibody against mTOR. Most preferably, those antibodies are as defined in Table 2.
  • the invention provides the use of one or more Renal Cell Carcinoma cell from an individual in predicting the prognosis of Renal Cell Carcinoma in the individual, comprising the step of determining the expression level in the one or more Renal Cell Carcinoma cell of at least three genes selected from those listed in Table A and, optionally, additionally based on the age of the individual.
  • the at least three genes comprises N-cadherin, EpCAM and mTOR. It is preferred that the sample is a tumour biopsy and the sample comprises tissue from two or more (and, preferably, three or more) distinct parts of the tumour. Exemplary approaches for performing such a use are discussed above.
  • the invention provides the use of one or more Renal Cell Carcinoma cell from an individual in predicting the progression of Renal Cell Carcinoma in the individual, comprising the step of determining the expression level in the one or more Renal Cell Carcinoma cell of at least three genes selected from those listed in Table A and, optionally, additionally based on the age of the individual. It is preferred that the at least three genes comprises N-cadherin, EpCAM and mTOR.
  • the sample is a tumour biopsy and the sample comprises tissue from two or more (and, preferably, three or more) distinct parts of the tumour. Exemplary approaches for performing such a use are discussed above.
  • the invention provides the use of one or more Renal Cell Carcinoma cell from an individual in predicting the response to therapy of Renal Cell Carcinoma in the individual, comprising the step of determining the expression level in the one or more Renal Cell Carcinoma cell of at least three genes selected from those listed in Table A and, optionally, additionally based on the age of the individual. It is preferred that the at least three genes comprises N-cadherin, EpCAM and mTOR. It is preferred that the sample is a tumour biopsy and the sample comprises tissue from two or more (and, preferably, three or more) distinct parts of the tumour. Exemplary approaches for performing such a use are discussed above.
  • the invention provides the use of one or more Renal Cell Carcinoma cell from an individual in selecting a treatment for the Renal Cell Carcinoma in the individual, comprising the step of determining the expression level in the one or more Renal Cell Carcinoma cell of at least three genes selected from those listed in Table A and, optionally, additionally based on the age of the individual. It is preferred that the at least three genes comprises N-cadherin, EpCAM and mTOR. It is preferred that the sample is a tumour biopsy and the sample comprises tissue from two or more (and, preferably, three or more) distinct parts of the tumour. Exemplary approaches for performing such a use are discussed above.
  • the invention provides the use of one or more Renal Cell Carcinoma cell from an individual in identifying an agent suitable for treating Renal Cell Carcinoma, characterised in that the one or more cell has an expression level of at least three genes selected from those listed in Table A, that is associated with poor prognosis and/or resistance to sunitinib. It is preferred that the at least three genes comprises N-cadherin, EpCAM and mTOR. It is preferred that the sample is a tumour biopsy and the sample comprises tissue from two or more (and, preferably, three or more) distinct parts of the tumour. Exemplary approaches for performing such a use are discussed above.
  • the invention further provides a method or a use or a kit substantially as claimed or described herein, with reference to the accompanying description and/or examples and/or drawings.
  • the listing or discussion in this specification of an apparently prior-published document should not necessarily be taken as an acknowledgement that the document is part of the state of the art or is common general knowledge. Preferred, non-limiting examples which embody certain aspects of the invention will now be described, with reference to the following figures:
  • FIG. 1 Expression of proteins differentially expressed in sunitinib-na ' fve and sunitinib- exposed tumours. The distribution of median expression values are shown in pairs for drug naive (left of each pair, light shading) and exposed (right of each pair, dark shading) tumour samples. Thirty proteins had significant expression differences (FDR p ⁇ 0.05).
  • Figure 2 Median expression values per tumour for proteins that had differential expression and substantively higher variance following sunitinib exposure. Values for sunitinib-exposed tumours are shown on the right (dark shading) and sunitinib-na ' fve on the left (light shading). This figure does not capture the intratumoral variance, because median expression values per tumour are plotted.
  • FIG. 5 Overall Survival distributions for the RCC_TRAIN (Su R) and RCC_TEST (SCOTRCC) cohorts.
  • the above distributions show bimodality for both cohorts studies, with very similar mode positions around 11 and 27 months.
  • RCC_TRAIN has largest proportion of patients in the mode centred around 27 months (reaching density value of 0.037).
  • RCC_TEST has largest proportion of patients in the mode around 11 months (reaching density value of 0.049). All data, including censored, are shown.
  • the proportion of patients in the long and short survival times differs between the groups.
  • the apparently larger proportion of patients with short survival in RCC_TEST is partly due to greater censoring in this cohort (55%) compared with RCC_TRAIN (41 %).
  • Figure 6 Within tumour variance ratio of sunitinib naive and sunitinib treated samples.
  • There are significantly more proteins with higher median variance in the treated group than the untreated group (P 0.00101 , binomial-test). Proteins with variance below zero on x- axis are greater for the untreated primary tumour, those above the x-axis zero were greater in the treated primary tumour. This result therefore demonstrates significantly increased molecular variance upon treatment.
  • Figure 8 Results for significantly variable and differentially expressed proteins in sunitinib naive and treated patient test and validation samples. (A) Box-and-whisker plot showing test set RPPA differential expression results of 4 key proteins. Medians and inter-quartile ranges are shown in the figure.
  • (B) Box-and-whisker plot showing AQUA evaluated protein expression of 4 key proteins using validation cohort of 61 sunitinib and 25 pazopanib treated and untreated paired mccRCC samples. Of the 4 proteins, CA9 was the only with significant differential protein expression (P 0.01). Medians and inter-quartile ranges are shown in the figure.
  • FIG. 9 - Array CGH and RNA interference CA9 results.
  • B RCC1 1 and
  • C CAKI-2 human RCC transfected with either control or CA9 siRNA, followed by sunitinib treatment and cell viability analysis 5 days later. Error bars represent standard errors of the mean. To confirm silencing, cell lysates from RCC11 and CAKI-2 siRNA transfected cells were analysed by western blotting using CA9 and ⁇ -actin- specific antibodies, as indicated.
  • FIG 10 Heatmap from aCGH data illustrating the recurrently lost region containing CA9 comprises 5.2MB on chromosome 9 from 32554042 to 38751914. CA9 locus is indicated.
  • FIG 11 Frequency plot illustrating quantity of aCGH gains (green) and losses (red) across the entire whole genome.
  • Figure 12 Overall approach for investigation of the effect of subsampling on NEAT predictive performance.
  • the distributions of hazard ratios and log-rank p-values (bottom right) across the 10 6 samples taken using a low-discrepancy sequence (methods) are shown in more detail in Figure 13.
  • Figure 13 Log hazard ratios (A) and log-rank p-values (B) from testing NEAT on RCC_TEST using a maximum of 1, 2 or 3 tumour samples from each patient, over one million sampling runs for each maximum. Protein concentrations for input to NEAT used median expression from the selected samples. Where patients had equal or fewer samples than the maximum indicated, all their samples were used. Input age values were unchanged. Hazard and p-values were determined by testing NEAT on the subsampled RCC_TEST cohorts. The vertical dot-dash lines indicates baseline NEAT performance using all available samples for each patient (log-hazard ratio, A). Using one sample per patient results in poor algorithm performance, although hazard ratios are significantly better than random; higher numbers of samples result in significantly improved performance (p ⁇ 10 "324 ).
  • FIG 14 Variation in per-patient NEAT risk score using a limited number of tumour samples.
  • Each plot shows the distribution of NEAT risk scores using every possible combination of tumour samples for a particular patient.
  • Vertical bars indicate log risk score (logRS) range using the specified number of samples. All patients had 2-8 samples available (median 4). The baseline risk using all samples is thus shown on the right of each plot as a single point.
  • logRS log risk score
  • logRS spread decreases as the number of samples increases.
  • Table 3 Multivariate Cox proportional-hazards results for overall survival using predictive variables identified by regularised wrapper selection on the SuMR mccRCC cohort (neo-adjuvant sunitinib).
  • Table 7 Patient demographics, pathology details and clinical outcomes of test and validation patient cohorts from whom there was adequate tumour tissue for molecular analysis, in Example 2.
  • IQR interquartile range
  • N/A not applicable
  • NA not available
  • Table 8 Validated antibodies used in RPPA and AQUA experiments in Example 2.
  • N-cadherin differs from other classical cadherins as a prognostic marker in renal cell carcinoma.
  • Sunitinib is an orally administered first-line treatment for metastatic clear cell Renal Cancer (mccRCC), and doubles median overall survival compared with immunotherapy [NEJM 2007;356:15, JCO 2009;27:3584].
  • Sunitinib targets tumour, endothelial and pericyte cells, where the mechanism of action includes competitive inhibition of multiple tyrosine kinases [Adv Ther 2012;29:202, Clin Cancer Res 2003;9:327].
  • intratumoural molecular heterogeneity appears highly prevalent in ccRCC [ ⁇ /ar Genef 2014;46:225, ⁇ /ar Rev Cancer 2012; 12:323, NEJM 2012; 366: 883], Cancer is a disease of dysregulated pathways; protein abundance and post-translational modifications are key determinants of pathway activity, thus functional proteomics is an attractive approach [Nat Med 2004; 10:789, Nat Rev Cancer 2010:10618]. However, cancer biomarker discovery has been dogged by failure [Cancer Res 2012;72:6079]. Intratumoural heterogeneity is a potential limiting factor in validation studies where molecular readouts are derived from sampling a small fraction of the overall tumour volume.
  • Variables were selected for Cox multivariate analysis using backward elimination regularised by Bayesian Information Criterion (BIC) on the RCC_TRAIN dataset, a form of wrapper feature selection (Schwarz 1978; Kohavi & John 1997).
  • BIC regularisation controls overfitting, which is particularly important when training data is limited (Zhang et al. 2010).
  • An initial Cox regression model was fitted using all features by the 'coxph' function from the R survival library (Therneau 2000).
  • Backward elimination iteratively removed a single feature at each step, selected for the greatest improvement in BIC value. Thus, features with low predictive power or high redundancy were removed. The procedure terminated with a final model when removing any single feature would not improve the BIC value.
  • the function 'stepAIC was used from the MASS R library, with the value of k (a multiplier penalising model complexity) specified for BIC regularisation (Venables & Ripley 2002).
  • NEAT N-cadherin
  • EpCAM EpCAM
  • EpCAM N-cadherin
  • age all have significant negative correlations with survival.
  • N- cadherin is normally expressed in proximal tubules of the kidney, the presumed origin of RCCs and is a marker of aggressiveness (Shimazui et al. 2006).
  • Canonical Epithelial to Mesenchymal Transition (EMT) involves gain of N-cadherin and EMT-like changes in RCCs correlate with poor prognosis(Harada et al. 2012; Pantuck et al. 2010; Thiery et al. 2009; Donhuijsen & Schulz 1989).
  • EpCAM expression is broadly associated with poor prognosis in cancers (Spizzo et al. 201 1 ; Trzpis et al. 2007), although in RCC reports link EpCAM with better prognosis especially in localised disease e.g. (Seligson et al. 2004; Eichelberg et al. 2013). Therefore, our finding that EpCAM expression correlates with RCC poor prognosis in a multivariate model differs from current thinking. This difference may be due to material analysed (i.e. sunitinib-exposed metastatic renal cancer tissue), and the technologies employed.
  • mTOR inhibitors are currently in clinical use (e.g. in USA), possibly in conjunction with sunitinib or agents with similar activity profile. Therefore a positive relationship with survival for mTOR is of immediate interest because this indicates that mTOR inhibitors may adversely affect outcome in patients where RTK-active drugs such as sunitinib are, or will be, part of the treatment schedule.
  • the NEAT algorithm performed well in stratifying ccRCC patients in both development (sunitinib-exposed) ( Figure 3) and validation (sunitinib-na ' ive) samples ( Figure 4).
  • the NEAT model outperformed both the DCM (Heng et al. 2013) and MSKCC (Motzer et al. 2002) nomograms on the RCC_TRAIN (SuMR) and RCC JEST (SCOTRRCC) cohorts, using hazard of 1 to stratify into high (above average) and low (below average) risk.
  • MSKCC approach is probably the most popular, although DCM has been found to be best-performing when compared with key current approaches (Heng et al. 2013).
  • the performance comparison results are summarised in Table 4; Net Reclassification Improvement (Pencina et al.
  • NEAT outperformed the DCM and MSKCC nomograms with respective values of 7.1% and 25.4%. All patients in RCC_TEST that were identified as high risk by NEAT experienced an event within 10 months or less. Therefore NEAT has very high estimated specificity for high risk patients at one or two years (100%). NEAT also achieves good sensitivity; in RCC_TEST the predicted high risk group contains 75% of patients experiencing an event within 1 year and 50% of those experiencing an event within 2 years.
  • Carbonic anhydrase 9 expression increases with VEGF targeted therapy and is predictive of outcome in metastatic clear cell renal cancer
  • OBJECTIVE To explore if dynamic, targeted therapy driven molecular changes correlate with mccRCC outcome.
  • Shortcomings include: selection of specific protein for analysis, and the specific time-points at which the treated tissue was analysed.
  • CONCLUSIONS CA9 levels increase with targeted therapy in mccRCC. Lower CA9 levels are associated with a poor prognosis and possible resistance, as indicated by the validation cohort.
  • PATIENT SUMMARY Drug treatment of advanced kidney cancer alters molecular markers of treatment resistance. Measuring CA9 levels may be helpful in determining which patients benefit from therapy.
  • VEGF targeted tyrosine kinase inhibitor (TKI) therapy is established as first line therapy in metastatic clear cell renal cancer (mccRCC) [1].
  • mccRCC metastatic clear cell renal cancer
  • Clinical benefit with sunitinib varies between mccRCC patients. While there are a number of prognostic clinical factors, there are presently few validated molecular means of improving prognosis or prediction of response of mccRCC patients to targeted therapies [2]; the recent report of serum IL-6 predicting response to pazopanib being an exception [3]. This lack of predictive ability is in contrast to numerous other tumour types, such as chronic myeloid leukaemia and breast cancer, where protein expression and mutation analysis can be used to predict response and treatment failure [4,5].
  • RNA interference (RNAi) in RCC cell lines addressed the functional relevance of significant changes.
  • Fresh frozen primary ccRCC tissue was obtained from the nephrectomy samples of 22 sunitinib naive mccRCC patients as part of the SCOTRRCC study (UK CRN ID: 12229). Tissue was also obtained from 27 mccRCC patients treated with 3 cycles of pre- surgical sunitinib (18 weeks) as part of the SuMR trial (NCT01024205), tissue from four of these patients was entirely necrotic, leaving 23 patients with adequate tissue for analysis (table 7). These two sample cohorts made up the test sample set.
  • TMA tissue microarray
  • Each piece of fresh frozen tumour tissue was mapped and separated into small pieces ( ⁇ 1 cm 3 ) from which lysates were created.
  • a frozen section was performed (MO) on each 1 cm 3 piece of tissue to confirm the presence of viable ccRCC and for grading. Where possible a minimum of four protein/DNA lysates were aimed for per patient.
  • 58 proteins were evaluated using RPPA. These proteins were relevant in RCC pathogenesis or sunitinib response and belonged to the following functional groups: cell cycle, apoptosis, protein kinases, angiogenesis, cell adhesion, PI3K pathway, epithelial- to-mesenchymal transition, MET/HGF and mismatch repair. There was no signal detected for three of the proteins (Ki67, FLT3 and phospho-Jak2). As such, 55 proteins were analysed in this study (antibodies detailed in Table 8). 2.4. Automated Quantitative Analysis (AQUA) of immunofluorescence
  • the CGH-segMNT module of NimbleScan was used for the analysis with a minimum segment length of five probes and an averaging window of 130kb.
  • Nimblegen arrays were positionally annotated based upon hg19 genomic coordinates and log ratio data was pre-processed in R as previously described [17]. Briefly, array data was normalised with print tip Loess from the limma package to produce normalised log ratios, filtered to remove outliers based upon a 1 MAD deviation of each probe from its immediate genomic neighbours and smoothed with a circular binary segmentation algorithm from the DNACopy package.
  • the human RCC cell lines CAKI-2 (wild type VHL) and RCC1 1 (VHL mutant) were transfected with a non-targeting control short interfering RNA (siRNA) (5'- CATGCCTGATCCGCTAGTC-3') or CA9 siRNA (5'-GAGGAGGATCTGCCCAGTGAA-3') (Qiagen, UK). Twenty-four hours after transfection, cells were either treated with 0.01 % DMSO or 4 ⁇ sunitinib and cell viability was assessed after 5 days using the Cell Titer Glo assay (Promega, UK). See supplementary methods for further RNAi methodology. 2.7. Statistical analysis
  • prognostic factors Heng prognostic score, Fuhrman grade, T stage at diagnosis, number of metastatic sites, age, and CA9 expression in nephrectomy ccRCC specimen
  • VEGF targeted therapy significantly alters the expression of a number of selected proteins despite protein ITH.
  • CA9 is a hypoxia-regulated transmembrane protein overexpressed in a number of cancers. It is usually associated with hypoxic stress and poor prognosis [19].
  • Tumour samples in the pre-VEGF TKI era showed conflicting data on the prognostic value of baseline CA9 [21 ,22].
  • Biomarker studies in the era of VEGF targeted therapy have failed to consistently show that high baseline CA9 protein levels are associated with a good outcome [24,25]. This may be because of the dynamic changes that occur with therapy and the use of archival tissue from a single time point for biomarker analysis.
  • the work presented here shows that not only does targeted therapy increase the expression of CA9 but these changing levels are also prognostic, these findings were observed in both inter-patient (unmatched test set) and critically the intra-patient (matched sequential biopsy and nephrectomy validation set) samples. This dynamic change in a prognostically important biomarker suggests the drive towards predictive biomarkers may be possible.
  • Anti-VEGF therapy is associated with vasoconstriction and subsequent hypoxia [8]; therefore, the up- regulation of CA9 with VEGF targeted therapy could be a consequence of effective VEGF targeting.
  • figure 7 shows that a number of VEGF and hypoxia associated markers are also affected by sunitinib (VEGFR-1 , VEGFR-3, PDGFR- ⁇ , c-KIT, VEGF-A, VEGF-D), supporting this argument.
  • sunitinib VEGFR-1 , VEGFR-3, PDGFR- ⁇ , c-KIT, VEGF-A, VEGF-D
  • HIF-1a was not differentially expressed, most likely due to its short half-life, which was exceeded by the warm ischaemia time during sample acquisition.
  • CA9 may have more of a direct oncogenic effect as a reaction to VEGF targeted therapy.
  • a biomarker, identified after a specific period of therapy is of potential clinical use providing patients are willing to have a repeat biopsy during treatment.
  • the utility of repeat biopsy in practice is challenging.
  • a randomised trial, comparing continued therapy with a change in therapy, in those patients who failed to gain a rise in CA9 with therapy would test this biomarker prospectively.
  • HUVECs were a gift from Kathryn Sangster of the Tissue Injury and Repair Group at the University of Edinburgh. HUVECs were cultures in EBM-2 media (Lonza Clonetics, UK) supplemented with endothelial cell growth kit (Lonza Clonetics, UK) and used in the RPPA experiments. RC124 human kidney cell (CLS, Cell Line Service, Germany) were also used in RPPA experiments and cultured in McCoy's 5A medium, 90% (Invitrogen, UK); fetal bovine serum (FBS), 10%.
  • the human renal cancer cell lines CAKI-2 and RCC11 a gift from Dr Tyson Sharp (Barts Cancer Institute, London, UK) were grown in RPMI medium (Sigma, UK) supplemented with FBS (10%) and antibiotics and used in RNAi experiments.
  • TMA construction The human renal cancer cell lines CAKI-2 and RCC11 , a gift from Dr Tyson Sharp (Barts Cancer Institute, London, UK) were grown in RPMI medium (Sigma, UK) supplemented with FBS (10%) and antibiotics and used in RNAi experiments. TMA construction
  • the TMA constructed using tissue from nephrectomy tissue was constructed using standardised techniques. 1mm tumour cores were used from at least 3 regions per tumour specimen [1]. For the biopsy TMA a technique previously described to create TMAs from biopsy specimens was utilized [2].
  • aCGH data was pre-processed on the basis of smoothed log ratios essentially as described [4,5].
  • raw Log2 ratios of intensity between samples and pooled female genomic DNA were read without background subtraction and normalised in the LIMMA package in R using PrinTipLoess.
  • Outliers were removed based upon their deviation from neighbouring genomic probes using an estimation of the genome-wide median absolute deviation of all probes.
  • a final dataset of 134937 probes with unambiguous mapping information according to the February 2009 build (hg19) of the human genome were used.
  • Log2 ratios were rescaled using the genome-wide median absolute deviation in each sample, and then smoothed using circular binary segmentation in the DNACopy package as previously described.
  • Losses and gains were defined as a circular binary segmentation (cbs)-smoothed Log2 ratio +/- 0.1.
  • Copy number thresholds for Nimblegen arrays used thresholds chosen based on an average genome wide median absolute deviation of 0.139 across all arrays, similar to those used in previous studies. These thresholds were determined as previously described and validated empirically by means of in situ hybridisation methods [6,7].
  • Gene amplification was defined as having a Log2 ratio > 0.45, corresponding to more than five copies. A categorical analysis was applied to the probes after classifying them as representing amplification (>0.45), gain (>0.1 and ⁇ 0.045), loss ( ⁇ -0.1), or no-change according to their cbs-smoothed Log2 ratio values.
  • Differential protein expression was assessed for the sunitinib naive and treated samples using established approaches.
  • the t-test may be applied when marker expression values are normally distributed and have equivalent variance (homoscedasticity). Therefore, to identify normally distributed markers we used the Lilliefors test, which examines the difference between the empirical and hypothetical (normal) cumulative distribution. The null hypothesis is that the data are normally distributed, for further detail, see [8]. Homoscedasticity was tested using the Fligner-Killeen test where the null hypothesis is that variances are equivalent, see [9]. For markers where Fligner-Killeen and Lillefors test results were consistent with the assumptions of homoscedasticity and normality the t-test was applied to assess differential expression.
  • the F-test is sensitive to deviation from normality and therefore was only applied where marker expression distributions were not significantly different to the normal distribution according to the Lilliefors test on both the naive and treated groups. Normality is also a requirement for ANOVA. Within-group homoscedasticity was assessed using the Fligner-Killeen test and proteins excluded from analysis unless they met this assumption in each of the treated and untreated groups, because homoscedasticity is a formal requirement for ANOVA. Again, P-values were corrected for false discovery rate according to the method of Benjamini and Hochberg. All P-values were two-tailed.
  • the NEAT algorithm performance was tested on modifications of RCC_TEST using a maximum number of tumour samples (MNTS) of 1 , 2 or 3 from each patient.
  • MNTS maximum number of tumour samples
  • the median of the selected tumour samples' protein expression values for each patient was used as input to NEAT.
  • Patient age was input unchanged. For patients with number of samples fewer than or equal to the current MNTS, all samples were used.
  • the hazard ratio and log-rank p-value for stratification into 'high' and 'low 1 risk groups were calculated by the NEAT algorithm on each subsampled cohort. The subsampling approach to assess is summarised in Figure 12.
  • the number of possible tumour sample combinations for each MNTS is given by the product of the number of sample combinations per patient: N, choose ⁇ 1 ,2,3 ⁇ , where N, indicates the number of samples available for patient i (2-8 for RCC_TEST) and ⁇ 1 ,2,3 ⁇ is the current MNTS.
  • N indicates the number of samples available for patient i (2-8 for RCC_TEST)
  • ⁇ 1 ,2,3 ⁇ is the current MNTS.
  • Each of these possibilities may be indexed by a unique integer; one million such integers were generated using a Sobol sequence, a quasi-random procedure ensuring low discrepancy [Sobol (1967) and Sobol 1976), supra].
  • each (master) integer generated by the sequence was calculated in two steps.
  • the master integer was converted to a set of integers, one integer for each patient, that represented the combinadic (combinatorial index) for each patient's sample combination. This was done by converting the master integer to a mixed radix number, where a radix equal to N, choose ⁇ 1 ,2,3 ⁇ was present for each patient, which is interpreted as a patient's sample combinadic.
  • each combinadic was mapped onto the specific samples for the corresponding patient for inclusion in the sampling run.
  • tumour samples were selected to be representative across the range of grades found in the tumour (low, mixed, high) and necrotic tissue was excluded.
  • Our results suggest that some prognostic and predictive methods evaluated at low sampling rates will have variable performance and suffer from low repeatability.
  • Approaches that capture tumour heterogeneity improve risk stratification of metastatic clear cell renal cell cancer specifically and furthermore, the sampling approach may be severely limiting in validation of novel predictive tools for cancer medicine.

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Abstract

La présente invention concerne des méthodes, des utilisations et des kits pour prédire le pronostic et/ou la progression d'un hypernéphrome chez un individu, et des méthodes, des utilisations et des kits permettant de prédire la réponse à une thérapie et/ou de sélectionner un traitement pour traiter un hypernéphrome chez un individu.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020178313A1 (fr) * 2019-03-05 2020-09-10 INSERM (Institut National de la Santé et de la Recherche Médicale) Nouveaux biomarqueurs et biocibles dans un carcinome des cellules rénales
CN113706538A (zh) * 2021-10-28 2021-11-26 中国医学科学院北京协和医院 确定肾细胞癌级别的系统及其应用
US11402382B2 (en) 2017-03-01 2022-08-02 Genentech, Inc. Diagnostic and therapeutic methods for cancer

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100222230A1 (en) * 2007-04-11 2010-09-02 The General Hospital Corporation Diagnostic and prognostic methods for renal cell carcinoma
US20130005597A1 (en) * 2009-12-18 2013-01-03 Rathmell W Kimryn Methods and compositions for analysis of clear cell renal cell carcinoma (ccrcc)

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100222230A1 (en) * 2007-04-11 2010-09-02 The General Hospital Corporation Diagnostic and prognostic methods for renal cell carcinoma
US20130005597A1 (en) * 2009-12-18 2013-01-03 Rathmell W Kimryn Methods and compositions for analysis of clear cell renal cell carcinoma (ccrcc)

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
A. J. ARMSTRONG ET AL: "Circulating Tumor Cells from Patients with Advanced Prostate and Breast Cancer Display Both Epithelial and Mesenchymal Markers", MOLECULAR CANCER RESEARCH, vol. 9, no. 8, 10 June 2011 (2011-06-10), pages 997 - 1007, XP055206642, ISSN: 1541-7786, DOI: 10.1158/1541-7786.MCR-10-0490 *
ALEXANDER LAIRD ET AL: "Differential Expression of Prognostic Proteomic Markers in Primary Tumour, Venous Tumour Thrombus and Metastatic Renal Cell Cancer Tissue and Correlation with Patient Outcome", PLOS ONE, vol. 8, no. 4, 5 April 2013 (2013-04-05), pages 1 - 14, XP055206601 *
GRANT D. STEWART ET AL: "What can molecular pathology contribute to the management of renal cell carcinoma?", NATURE REVIEWS UROLOGY, vol. 8, no. 5, 12 April 2011 (2011-04-12), pages 255 - 265, XP055206609, ISSN: 1759-4812, DOI: 10.1038/nrurol.2011.43 *
SAMIRA A. BROOKS ET AL: "ClearCode34: A Prognostic Risk Predictor for Localized Clear Cell Renal Cell Carcinoma", EUROPEAN UROLOGY, vol. 66, no. 1, 25 February 2014 (2014-02-25), pages 77 - 84, XP055206732, ISSN: 0302-2838, DOI: 10.1016/j.eururo.2014.02.035 *

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US11402382B2 (en) 2017-03-01 2022-08-02 Genentech, Inc. Diagnostic and therapeutic methods for cancer
WO2020178313A1 (fr) * 2019-03-05 2020-09-10 INSERM (Institut National de la Santé et de la Recherche Médicale) Nouveaux biomarqueurs et biocibles dans un carcinome des cellules rénales
CN113706538A (zh) * 2021-10-28 2021-11-26 中国医学科学院北京协和医院 确定肾细胞癌级别的系统及其应用

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